Self-Hosting vs Cloud GPU: When Does Buying Your Own Hardware Pay Off?

Cloud GPUs are expensive if you run 24/7. A single A100 instance costs about $1,366/month on AWS ($1.90/hr × 24 × 30). At that rate, an A100 card ($12,000-$15,000 retail) pays for itself in about 10 months.

But the real math is more complicated.

The Hardware Costs

Component	Cost
NVIDIA A100 80GB (PCIe)	~$13,000
Server (dual Xeon, 512GB RAM, PSU)	~$4,000
Networking (10GbE switch, NICs)	~$800
Rack/cooling/power setup	~$1,500
Total upfront	~$19,300

This is for one GPU. If you need 2-4 GPUs, the server cost increases but per-GPU cost drops. A 4× A100 server costs about $57,000 upfront.

The Ongoing Costs

Item	Monthly
Electricity (A100 draws ~300W under load × 24h × $0.12/kWh)	$26
Cooling (HVAC overhead, ~30% of power)	$8
Colocation (1U rack space, power, bandwidth)	$100-200
Internet (business fiber, 1Gbps)	$100-300
Hardware maintenance (spare parts, failures)	~$100 amortized
Total monthly	~$450

Break-Even Analysis

1× A100, 24/7 inference:

Approach	Months 1-12	Months 12-24	Months 24-36
Cloud (AWS, on-demand)	$16,392	$16,392	$16,392
Cloud (AWS, 1yr reserved)	$12,000	$12,000	$12,000
Self-hosted	$24,700	$5,400	$5,400

Break-even:

• vs on-demand: month 15
• vs reserved: month 20

If you run the GPU 24/7 for more than 15-20 months, buying beats renting. If you run it for less, cloud wins.

What This Analysis Misses

Cloud Advantages (Not in the Math)

• No upfront capital: $19,300 at once vs monthly billing
• Scalability: Need 4 more GPUs next week? Cloud gives them. Self-hosted: order hardware, wait 4-6 weeks.
• No hardware obsolescence risk: NVIDIA releases a new GPU generation. Your cloud provider upgrades. Your self-hosted A100 is now a depreciating asset.
• Geographic distribution: 5 regions costs the same as 1 region ($0 more per region). Self-hosting in 5 locations costs 5×.
• No hardware failure risk: Cloud handles dead GPUs, PSU failures, RAM errors. Self-hosting: you handle them.
• No staffing overhead: Someone has to manage hardware, even if it's just you. Cloud: none.

Self-Hosting Advantages

• Fixed cost: $19,300 upfront, then $450/month. Predictable budgeting.
• No rate limits: Run at 100% utilization 24/7 with no throttling. Cloud providers don't throttle, but spot instances can be reclaimed.
• Data sovereignty: Hardware is physically in your location. No cloud provider has access.
• No vendor dependency: You're not tied to AWS/GCP/Azure pricing changes. NVIDIA raised prices? Too late, you already own the card.
• Full control: Overclock if you want. Custom cooling. Direct hardware access for performance tuning.

The Practical Threshold

Self-hosting starts making financial sense at:

• 3+ GPUs running 24/7 — at this scale, the cloud bill is $3,500-$5,500/month. Hardware pays off in under 12 months.
• Predictable, stable workloads — fine-tuning jobs, batch inference, always-on endpoints
• Existing data center/office space — if you already pay for space and power, the incremental cost is lower
• In-house hardware expertise — if nobody on the team has managed server hardware, factor in the learning curve

For most ML teams deploying 1-4 models, cloud is still cheaper when you account for the hidden costs of self-hosting (your time, reliability, scaling flexibility).

The Hybrid Approach

Run baseline workloads on owned hardware. Burst to cloud for spikes. This is how most mid-size ML teams operate:

• 2× A100s in colocation: handles steady traffic (80% of requests)
• Cloud GPU instances for spikes: handles peak traffic (20% of requests)
• Roptal routes traffic across both based on utilization and cost

This gives you the cost advantage of owned hardware without the scaling limitations.